fix bug in xgboostImpute() for classifying categorical variable with …

…containting 2 value; added drawing from probability for xgboostImpute()

R/xgboostImpute.R

@@ -59,11 +59,16 @@ xgboostImpute <- function(formula, data, imp_var = TRUE,
       currentClass <- NULL
       if(inherits(labtmp,"factor")){
         currentClass <- "factor"
+
+        predict_levels <- levels(labtmp)
         labtmp <- as.integer(labtmp)-1
         if(length(unique(labtmp))==2){
           objective <- "binary:logistic"
+          predict_levels <- predict_levels[unique(labtmp)+1]
+          labtmp <- as.integer(as.factor(labtmp))-1
+
         }else if(length(unique(labtmp))>2){
-          objective <- "multi:softmax"
+          objective <- "multi:softprob"
           num_class <- max(labtmp)+1
         }
 
@@ -72,7 +77,7 @@ xgboostImpute <- function(formula, data, imp_var = TRUE,
         if(length(unique(labtmp))==2){
           lvlsInt <- unique(labtmp)
           labtmp <- match(labtmp,lvlsInt)-1
-          warning("binary factor detected but not probproperlyably stored as factor.")
+          warning("binary factor detected but not properly stored as factor.")
           objective <- "binary:logistic"
         }else{
           objective <- "count:poisson"## Todo: this might not be wise as default
@@ -93,27 +98,38 @@ xgboostImpute <- function(formula, data, imp_var = TRUE,
       mm <- model.matrix(form,dattmp)
       if(!is.null(num_class)){
         mod <- xgboost::xgboost(data = mm, label = labtmp,
-                                nrounds=nrounds, objective=objective, num_class = num_class, verbose = verbose,...)
+                                nrounds=nrounds, objective=objective, num_class = num_class, verbose = verbose, ...)
       }else{
         mod <- xgboost::xgboost(data = mm, label = labtmp,
-                                nrounds=nrounds, objective=objective, verbose = verbose,...)
+                                nrounds=nrounds, objective=objective, verbose = verbose, ...)
       }
 
       if (verbose)
         message("Evaluating model for ", lhsV, " on ", sum(!rhs_na & lhs_na), " observations")
+
       predictions <- 
-        predict(mod, model.matrix(formPred,subset(data, !rhs_na & lhs_na)))
-      if(currentClass=="factor"){
-        if(is.null(num_class)){
-          data[!rhs_na & lhs_na, lhsV] <- levels(dattmp[,lhsV])[as.numeric(predictions>.5)+1]  
+        predict(mod, newdata = model.matrix(formPred,subset(data, !rhs_na & lhs_na)), reshape=TRUE)
+
+      if(objective %in% c("binary:logistic","multi:softprob")){
+
+        if(objective =="binary:logistic"){
+          predictions <- cbind(1-predictions,predictions)
+        }
+
+        predict_num <- 1:ncol(predictions)
+        predictions <- apply(predictions,1,function(z,lev){
+          z <- cumsum(z)
+          z_lev <- lev[z>runif(1)]
+          return(z_lev[1])
+        },lev=predict_num)
+
+        if(is.factor(dattmp[[lhsV]])){
+          predictions <- predict_levels[predictions]
         }else{
-          data[!rhs_na & lhs_na, lhsV] <- levels(dattmp[,lhsV])[predictions+1]  
+          predictions <- lvlsInt[predictions]
         }
-      }else if(currentClass%in%c("numeric","integer")&objective=="binary:logistic"){
-        data[!rhs_na & lhs_na, lhsV] <- lvlsInt[as.numeric(predictions>.5)+1]  
-      }else{
-        data[!rhs_na & lhs_na, lhsV] <- predictions  
       }
+      data[!rhs_na & lhs_na, ][[lhsV]] <- predictions  
 
     }